Which process converts data into a common format to enable blending?

The process that converts data into a common format to enable blending is known as data transformation. This step is crucial in data preparation, particularly when dealing with datasets that originate from different sources or come in varied formats. Data transformation ensures that all data points share a uniform structure and consistent definitions, which is essential for accurate analysis and reporting.

During the transformation process, tasks such as cleaning, normalizing, aggregating, and reshaping data are performed. These operations help to reconcile discrepancies and ensure compatibility among the datasets. By standardizing data formats and structures, data transformation facilitates seamless blending, allowing analysts to derive insights from combined datasets effectively.

Data mapping, while related, focuses more on defining how fields from one dataset correspond to fields in another and does not necessarily deal with the actual conversion of data formats. Data aggregation refers to summarizing data points, which does not directly address the need for a common format. Data analysis, on the other hand, involves exploring and interpreting data, which occurs after the data has been transformed and blended. Thus, data transformation is the correct answer because it specifically addresses the need for converting diverse datasets into a common format prior to analysis.